Data cleaning consists primarily in implementing error prevention strategies before they occur (see data quality control procedures later in the document). However, error-prevention strategies can reduce but not eliminate common errors and many data errors will be detected incidentally during activities such as:
When collecting or entering data
When transforming/extracting/transferring data
When exploring or analyzing data
When submitting the draft report for peer review
There are many benefits to having clean data:
1. It removes major errors and inconsistencies that are inevitable when multiple sources of data are being pulled into one dataset.
2. Using tools to clean up data will make everyone on your team more efficient as you’ll be able to quickly get what you need from the data available to you.
3. Fewer errors mean happier customers and fewer frustrated employees.
4. It allows you to map different data functions, better understand what your data is intended to do, and learn where it is coming from.
Clean data means clear direction
Good decisions, bad decisions: they all hinge upon the quality of the data that informs them. Errors cost money, take time to correct, and can damage your brand. Data cleansing is one way to make sure that you can trust the data that your business relies on. And when you trust your data, you can make decisions with accuracy, precision, and confidence.
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